Why are my submissions not accepted by top conferences, while similar papers from others are accepted? I'm doing my PhD in computer science. I had a few tries for top rank conferences like IJCAI, CVPR and ICML but my paper was rejected from them and consequently I sent the papers to second tier conferences for the sake of having publications. 

However when I compare my work with some accepted papers (in previous years or current year) in those conferences, I see similar technical level or novelty in them comparing to mines. 

For example reviewers more often mentioned I just added something to the pre-existed approaches or combined already existing things from the literature to shape an algorithm, which I noticed is a common case notable number of papers in the top computer science conferences. 

One thing I do not have in my papers is bringing unnecessary but sophisticated math jargon to show something important is happening. For example, I noticed in other's papers although the contribution was for instance adding an extra objective term in the optimization framework, they used complicated algebraic representations or visualization techniques to related their contribution to some bigger underlying phenomena! But the short answer is they did it because it obviously should make the outcome result better. 

From another point I'm not sure if I try another top conference (with a new work) and my paper gets rejected, then would it damage my or my supervisor's reputation in the field?

So although I still have great enthusiasm to try for the next relevant top conference, I got a feeling that I'm missing an important ingredient from the recipe! 

PS: My advisor is not so competent in this regard and I'm among the first group of his PhD students and you can guess the rest.
One thing I do not have in my papers is bringing unnecessary but sophisticated math jargon to show something important is happening. For example, I noticed in other's papers although the contribution was for instance adding an extra objective term in the optimization framework, they used complicated algebraic representations or visualization techniques to related their contribution to some bigger underlying phenomena! But the short answer is they did it because it obviously should make the outcome result better.


I can think of several ways to interpret this observation, some more charitable than others.


Computer science research papers are not about the results per se, but about the techniques developed to attain those results. It's possible that in the revirewers' eyes, the apparent connection to the larger underlying phenomenon is the main contribution of the paper.  The main contribution is not merely that the outcome is better, but at least an attempted explanation of why the outcome is better.
People are bad judges of the quality their own research results. More to the point: Your opinion of your research is irrelevant; only your peers' opinions actually matter.  If they find your papers less interesting than others, then by definition your papers are less interesting.
Emerson was wrong.  The world will not beat a path to your door if you merely build a better mousetrap.  You have to sell your results.  For your papers to be accepted, they must follow the cultural expectations of your audience.  If the IJCAI/ICML/CVPR audience expects a certain level of mathematical sophistication, then papers that do not display that sophistication are less likely to be accepted, even if that sophistication is unnecessary.
It is very easy to confuse mathematical depth/complexity with importance/difficulty. Top theoretical computer science conferences have a reputation for preferring more mathematically "difficult" papers to papers using more elementary techniques, and lots of reviewers assume without justification that any result that appears straightforward in retrospect must have been easy to derive.  But unless someone proves that P=NP, "trivial" is different from "nondeterministically trivial".
Because CVPR, ICML, and IJCAI are enormous conferences with low acceptance rates, acceptance decisions have high variance.  Given the breadth and complexity of the field, the ridiculous number of submissions, and the limited time for reviews, it is impossible for the program committee to make fully informed judgements about every submission.  There is an element of randomness even at smaller conferences, but for larger prestigious conferences, the randomness overwhelms the actual "signal".  In 2014, NIPS ran an experiment with two independent program committees; most papers accepted by one committee were rejected by the other.  It amazes me that anyone found this result surprising.
PC members are apes; they do apey things. As in any other large community, there are sub-communities within machine learning that prefer their own papers to others.  Even though submissions are blinded, reviewers can identify tribal affiliation—if only subconsciously—by writing style, citation patterns, choice of method, choice of data set, or choice of evaluation metrics.  If you're not in the right tribe, your papers are less likely to be accepted.